I recall that at some meeting circa mid-2012, I was part of a group discussing the results of some analysis or other, when one of the people around the table sounded off with a hint of exasperation mixed with a tinge of fright, this isn't one of those neural networks, is it? I knew of his past run-ins with and deep-seated anxiety about neural networks, so I assuaged his fears making some sarcastic comment that neural networks have basically gone the way of the dinosaur. No one disagreed! Several months later, I was gobsmacked when I attended a local meeting where the discussion focused on, of all things, neural networks and this mysterious deep learning. Machine learning pioneers such as Ng, Hinton, Salakhutdinov, and Bengio have revived neural networks and improved their performance.

Much media hype revolves around these methods with high-tech companies such as Facebook, Google, and Netflix investing tens, if not hundreds, of millions of dollars. The methods have yielded promising results in voice recognition, image recognition, and automation. If self-driving cars ever stop running off the road and into each other, it will certainly be from the methods discussed here.

In this chapter, we will discuss how the methods work, their benefits, and inherent drawbacks so that you can become conversationally competent about them. We will work through a practical business application of a neural network. Finally, we will apply the deep learning methodology in a cloud-based application.